Dalai's note: A piece by Dr. Richard Gunderman posted on TheHealthcareBlog.com. It is unclear whether or not Dr. Gunderman's "discovery" is a real document or not. Still, it would seem to explain a lot of what we are seeing in healthcare today...
Dalai's note: Here is another piece cross published from KevinMD.com. I have a huge level of antipathy toward "Value-Based" reimbursement. From the beginning, I smelled a rat. How could we in radiology in particular prove the "value" of what we do in a manner that would convince those who hold the purse strings that we should actually be paid for our efforts? If, for example, we tell the ER doc that his order for a CT is inappropriate, we save the system money, and risk a lawsuit. If we let it go through, and it is negative as expected, we are dinged for charging the system for something that didn't produce "value". In other words, we are screwed either way. What follows is a much better analysis of a sorry situation...
I feel like we’re at almost the same place with Apple iWatch as we were when we were concerned the first iPhone wouldn’t have a keyboard.
At first I didn’t quite get it with the Apple Watch: a computer on a wrist. I don’t like wearing a watch, what’s it going to do for me? At the same announcement where Apple was going with larger versions of the iPhone, Apple debuts a much smaller screen and perhaps the smallest interface yet. How could the interface get smaller and this be so revolutionary at a time when interfaces are getting bigger?
But, in fact, the interface just got a lot bigger.
It’s the combination of NFC, iBeacon and Bluetooth 4.0, along with the fact that the device knows where it is (on the wrist) and can communicate with a phone and many other things. Apple is mapping our physical world and making our close physical environment the interface, and it’s largely transparent to the user.
From Business Insider on Apple Watch Tracking our Movements:
“Specifically, the tracking mechanism on Apple Watch will be similar to the little-understood mechanism currently used in Apple’s iPhones. It combines Bluetooth 4.0 (the localized wireless signal system that lets you transmit or receive data from a nearby device), iBeacon (signal-emitting beacons that Apple is seeding all over stores and other physical locations in the real world) and NFC (another wireless transmitting system that Apple has incorporated into iPhone 6 and iPhone 6 Plus, and the Apple Watch).”
The Watch will be able to close the loop on activity between you, messages you receive and payments you make (via Apple Pay) and even how different environments, messages and products make you feel, and how your respond in terms of activity.
In other words, it will not only allow Apple to monetize the immediate context and environment, but it will be able to amass a wealth of data on both your bodily activity and even emotion and place in the world. This will help us understand when people will be in the most receptive mood to receive a message. Your responses and activities have just become part of the network, a part of the economy. Apple is building a behavioral economy.
And it’s going to happen fast. Banks are already heavily advertising Apple Pay, and Apple has a jump on mapping all of our internal spaces. When you want to make the world an interface, you need a very detailed map of it.
How Apple manages all this information will determine whether it’s convenient or creepy. They’ll need to maintain very tight trust with consumers. In the post-Jobs era of Apple, Apple will be able to resist the temptation to share this wealth of behavioral economic data?
Putting the World into a Health Context
Apple is seeking to map our our worlds both physically and via several other contexts. It says it right in the press release “the most personal device ever”. When Apple says personal, they mean they understand your context. They know who you are, what you’re doing, and even what you want to do within that context. Apple acquired . Think of it as digital psychology, and they’ll be able to run tests every moment of every day.
There are many different ways to view a person’s context and they’ll often wind up in dimensions. Wellness organizations often talk about the “8 dimensions of wellness”: Emotional, Environmental,Financial, Intellectual, Occupational, Physical, Social and Spiritual.
With the Watch, Apple could have a pretty good map of each of these for any individual. As we make the move from patient to consumer, and from health care to health, this kind of contextual awareness will become critical.
Beyond beacons and bluetooth, part of this ability comes from the idea that a device on the wrist can tell much more about what I’m doing than a phone can. A phone doesn’t know where it is, a watch usually does, particularly when it can communicate with a phone. It can also help the phone understand where it is.
With HealthKit and iWatch together, we have the potential not only for an environmental interface, but also a body interface, sensing the user’s internal state, could be a first step toward the internet of things around consumers as Tim Cook hinted.
At VivaPhi we identify several different contexts and environments of influence in consumer’s lives and how they make decisions. The Watch will be able to say more about these various contexts and exactly how these contexts, messages and tools are able to influence behavior. We see the potential as a contextual platform on a wrist. We’ll see what app developers create before we have much of an idea of what it can do, whether it’s convenient or creepy. What happens over the next few years will be as surprising or more so than the iPad apps.
The Apple Watch has little to do with telling time, it’s about contextual awareness. It’s a step toward our entire world becoming an interface. Let’s hope Apple can keep the streak alive creating devices that are more about what the consumer wants the world to do, rather than what the world wants the consumer to do.
On Sept. 1, we started the 20 Question for Health IT project, which spanned 20 weekdays and included insights from different contributors on various health IT topics, ranging from the always-popular interoperability to off-the-radar topics such as Bitcoin and even a joint clinician/patient EHR system. After taking a step back to view all 20 questions in one place, I am quite pleased we were able to attract so many different, intelligent perspectives from across the health IT landscape.
I would like to thank the 19 other contributors to this project. Please take a minute to send your thanks by following them all on Twitter — I’m confident they’ll continue to educate and inspire us to tackle the difficult questions needed to improve health IT.
You can view each question in the following presentation – please share it with your colleagues and connections to continue the conversation.
Question from Brian Eastwood, senior editor at CIO Online:
Healthcare is full of individual instances of data analytics “wins” – Hospital X reducing readmissions, Health System Y cutting prescription costs or Practice Z streamlining its bulk buying of tongue depressor. How does healthcare move from easy wins in analytics to more tangible, repeatable results?
It’s hard to find a healthcare conference without a big data track these days. Most of the presentations focus on what a particular institution did, not how it did what it did. There’s certainly a place for such case studies, as they can inspire healthcare leaders to look at existing problems in news ways. In focusing on ‘what’ and not ‘how,’ though, healthcare runs the risk of keeping the expertise that’s needed to pull off a successful data analytics initiative as siloed as the data itself. We can’t talk about healthcare data analytics without getting down and dirty—discussing how to sell it to executives, how to allocate resources, how to disseminate and interpret the results and, critically, how to make analytics an integral part of an institution’s business strategy.
Healthcare executives are continuously evaluating the subject of RFID and RTLS in general. Whether it is to maintain the hospitals competitive advantage, accomplish a differentiation in the market, improve compliance with requirements of (AORN, JCAHO, CDC) or improve asset utilization and operating efficiency. As part of the evaluations there is that constant concern around a tangible and measurable ROI for these solutions that can come at a significant price.
When considering the areas that RTLS can affect within the hospital facilities as well as other patient care units, there are at least four significant points to highlight:
Disease surveillance: With hospitals dealing with different challenges around disease management and how to handle it. RTLS technology can determine each and every staff member who could have potentially been in contact with a patient classified as highly contagious or with a specific condition.
Hand hygiene compliance: Many health systems are reporting hand hygiene compliance as part of safety and quality initiatives. Some use “look-out” staff to walk the halls and record all hand hygiene actives. However, with the introduction of RTLS hand hygiene protocol and compliance when clinical staff enter or use the dispensers can now be dynamically tracked and reported on. Currently several of the systems that are available today are also providing active alters to the clinicians whenever they enter a patient’s room and haven’t complied with the hand hygiene guidelines.
Locating equipment for maintenance and cleaning:
Having the ability to identify the location of equipment that is due for routine maintenance or cleaning is critical to ensuring the safety of patients. RTLS is capable of providing alerts on equipment to staff.
A recent case of a hospital spent two months on a benchmarking analysis and found that it took on average 22 minutes to find an infusion pump. After the implementation of RTLS, it took an average of two minutes to find a pump. This cuts down on lag time in care and can help ensure that clinicians can have the tools and equipment they need, when the patient needs it.
There are also other technologies and products which have been introduced and integrated into some of the current RTLS systems available.
There are several RTLS systems that are integrated with Bed management systems as well as EHR products that are able to deliver patient order status, alerts within the application can also be given. This has enabled nurses to take advantage of being in one screen and seeing a summary of updated patient related information.
Unified Communication systems:
Nurse calling systems have enabled nurses to communicate anywhere the device is implemented within the hospital facility, and to do so efficiently. These functionalities are starting to infiltrate the RTLS market and for some of the Unified Communication firms, it means that their structures can now provide a backbone for system integrators to simply integrate their functionality within their products.
In many of the recent implementations of RTLS products, hospital executives opted to deploy the solutions within one specific area to pilot the solutions. Many of these smaller implementations succeed and allow the decision makers to evaluate and measure the impacts these solutions can have on their environment. There are several steps that need to be taken into consideration when implementing asset tracking systems:
• Define the overall goals and driving forces behind the initiative
• Develop challenges and opportunities the RTLS solution will be able to provide
• Identify the operational area that would yield to the highest impact with RTLS
• Identify infrastructure requirements and technology of choice (WiFi based, RFID based, UC integration, interface capability requirements)
• Define overall organizational risks associated with these solutions
• Identify compliance requirements around standards of use
RFID is one facet of sensory data that is being considered by many health executives. It is providing strong ROI for many of the adapters applying it to improve care and increase efficiency of equipment usage, as well as equipment maintenance and workflow improvement. While there are several different hardware options to choose from, and technologies ranging from Wi-Fi to IR/RF, this technology has been showing real value and savings that health care IT and supply chain executives alike can’t ignore.
It was not long after mankind invented the wheel, carts came around. Throughout history people have been mounting wheels on boxes, now we have everything from golf carts, shopping carts, hand carts and my personal favorite, hotdog carts. So you might ask yourself, “What is so smart about a medical cart?”
Today’s medical carts have evolved to be more than just a storage box with wheels. Rubbermaid Medical Solutions, one of the largest manufacturers of medical carts, have created a cart that is specially designed to house computers, telemedicine, medical supply goods and to also offer medication dispensing. Currently the computers on the medical carts are used to provide access to CPOE, eMAR, and EHR applications.
With the technology trend of mobility quickly on the rise in healthcare, organizations might question the future viability of medical carts. However a recent HIMSS study showed that cart use, at the point of care, was on the rise from 26 percent in 2008 to 45 percent in 2011. The need for medical carts will continue to grow; as a result, cart manufacturers are looking for innovative ways to separate themselves from their competition. Medical carts are evolving from healthcare products to healthcare solutions. Instead of selling medical carts with web cameras, carts manufacturers are developing complete telemedicine solutions that offer remote appointments throughout the country, allowing specialist to broaden their availability with patients in need. Carts are even interfaced with eMAR systems that are able to increase patient safety; the evolution of the cart is rapidly changing the daily functions of the medical field.
Some of the capabilities for medical carts of the future will be to automatically detect their location within a healthcare facility. For example if a cart is improperly stored in a hallway for an extended period of time staff could be notified to relocate it in order to comply to the Joint Commission’s requirements. Real-time location information for the carts could allow them to automatically process tedious tasks commonly performed by healthcare staff. When a cart is rolled into a patient room it could automatically open the patient’s electronic chart or give a patient visit summary through signals exchanged between then entering cart and the logging device kept in the room and effectively updated.
Autonomous robots are now starting to be used in larger hospitals such as the TUG developed by Aethon. These robots increase efficiency and optimize staff time by allowing staff to focus on more mission critical items. Medical carts in the near future will become smart robotic devices able to automatically relocate themselves to where they are needed. This could be used for scheduled telemedicine visits, the next patient in the rounding queue or for automated medication dispensing to patients.
Innovation will continue in medical carts as the need for mobile workspaces increase. What was once considered a computer in a stick could be the groundwork for care automation in the future.
This has been an eventful year for speech recognition companies. We are seeing an increased development of intelligence systems that can interact via voice. Siri was simply a re-introduction of digital assistants into the consumer market and since then, other mobile platforms have implemented similar capabilities.
In hospitals and physician’s practices the use of voice recognition products tend to be around the traditional speech-to-text dictation for SOAP (subjective, objective, assessment, plan) notes, and some basic voice commands to interact with EHR systems. While there are several new initiatives that will involve speech recognition, natural language understanding and decision support tools are becoming the focus of many technology firms. These changes will begin a new era for speech engine companies in the health care market.
While there is clearly tremendous value in using voice solutions to assist during the capture of medical information, there are several other uses that health care organizations can benefit from. Consider a recent product by Nuance called “NINA”, short for Nuance Interactive Natural Assistant. This product consists of speech recognition technologies that are combined with voice biometrics and natural language processing (NLP) that helps the system understand the intent of its users and deliver what is being asked of them.
This app can provide a new way to access health care services without the complexity that comes with cumbersome phone trees, and website mazes. From a patient’s perspective, the use of these virtual assistants means improved patient satisfaction, as well as quick and easy access to important information.
Two areas we can see immediate value in are:
Customer service: Simpler is always better, and with NINA powered Apps, or Siri like products, patients can easily find what they are looking for. Whether a patient is calling a payer to see if a procedure is covered under their plan, or contacting the hospital to inquire for information about the closest pediatric urgent care. These tools will provide a quick way to get access to the right information without having to navigate complex menus.
Accounting and PHR interaction: To truly see the potential of success for these solutions, we can consider some of the currently used cases that NUANCE has been exhibiting. In looking at it from a health care perspective, patients would have the ability to simply ask to schedule a visit without having to call. A patient also has the ability to call to refill their medication.
Nuance did address some of the security concerns by providing tools such as VocalPassword that will tackle authentication. This would help verify the identity of patients who are requesting services and giving commands. As more intelligence voice-driven systems mature, the areas to focus on will be operational costs, customer satisfaction, and data capture.
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By some accounts, almost 30% of EHR users will be interested in replacing their software as they move from Meaningful Use Stage 1 to 2 to 3 over the next few years. Although I’ve written and spoken extensively in the past about how to make sure you pick the right digital health and EHR software, I wanted to put together a new “common sense” type of Do’s and Don’ts list for picking new EHR software. To help me out, I reached out to J.P. Medved at Capterra because of their unique expertise in this area. What follows are the dos and don’ts of each step in the EHR buying process; these tips will guide you when comparing options so you select medical software best suited for your needs. They may also serve as a guide for software vendors who want to stand out from the crowd. Here’s what J.P. said:
What other dos and don’ts are worth including in this list? Have you already gone through the EHR purchase process and wish they did something differently? As a vendor have you observed buyers making mistakes in their selection research? Add your thoughts in the comments!
Editor’s note: You can follow J.P. on Twitter at @rizzleJPizzle.
I wrote my first patient portal site, built into my first EMR software, back in 1998. At that time I mistakenly thought that portals would take off and patients would embrace them. What I quickly learned was that patient portals aren’t really portals in the sense of Yahoo! or Google but enterprise software’s customer-facing front-ends. The enterprise software in this case is of course an EHR and the customers are the patients. If patients are the consumers then their expectations are that they can conduct “business” with the practice through the portal. This means messaging, getting questions answers, scheduling appointments, reviewing all records, etc. are minimal value propositions for a patient portal. However, even though portals are terrific opportunities for patient engagement, most portals’ technology architecture do not provide significant enough value for patients. I reached out to Cameron Graham, Editorial Coordinator at TechnologyAdvice, about what he’s seeing in the marketplace when it comes to portals. Cameron oversees market research for healthcare IT, business intelligence, and other emerging technologies and here’s what he had to say about how vendors and clinicians can encourage patients to use their respective portals:
Patient portals have the potential to simplify practice operations and reduce physician costs, but only if patients adopt and use them. We surveyed 430 people who had recently visited their primary care doctor in order to gauge how patients wish to communicate with their physician. Here some of the key takeaways from our survey for vendors and practices.
1. Physicians need to raise awareness about patient portal availability and benefits.
In our survey, 39.9% of patients said they didn’t know whether their primary care physician offered patient portal software. Given the high rates of EHR adoption among primary care physicians, and the increasing amount of EHRs that contain integrated portals, this is likely due to a lack of marketing. The responsibility for such marketing falls on physicians. Practices should consider implementing in-office programs to walk patients through available portal software, or deliver clear instructions to them prior to an appointment. Physicians should not assume that patients will investigate their websites or find such software on their own, even if told about it. At the very least, physician’s should provide each patient with an instruction sheet at the end of an appointment. Meaningful Use stage 2 requires that five percent of a practice’s patients utilize such a service. This threshold will only rise going forward, making effective promotion of such tools even more important.
To help promote increased patient awareness, vendors should consider investing in their own marketing campaigns, outside of any physician-led campaigns. Vendor involvement with EHR and health IT software is usually limited to the installation phase. However, it is in vendor interest to encourage patient awareness of such technology. Supplying practices with marketing materials, providing post-implementation support for customers, or even compiling an online list of each practice using their software could go a long way toward boosting awareness.
2. Patients still prefer hearing from physicians by phone, rather than through a portal, but younger patients are open to online appointment scheduling.
Our results found that just 13.6 percent of patients want to be contacted through a patient portal for general communications, while 14.1 percent wish to receive lab results and diagnoses through such a site. On the other hand, 42.7 percent want their doctor to deliver test results over the phone, and 42.9 percent want to receive general communication by phone as well. These preferences were consistent between age groups, except when it came to online appointment scheduling. 63.6 percent of patients aged 18-24 said they preferred using an online calendar to schedule appointments, while only 15 percent of respondents over the age of 65 chose the same.
These results suggest that physicians and vendors need to provide more incentive for patients to use online resources, if they want to move to a digital-first practice model. This could be done through increased marketing to consumers by vendors, or through physician led campaigns touting the benefits of online systems. Alleviating any security-concerns patients may have about posting health information online will likely need to be priority in these efforts. Younger patients may also be more receptive to portals, as they already prefer to schedule their appointments online.
Vendors should also place greater emphasis on the usability and intuitiveness of their patient portal interfaces. This would lower the barrier-to-entry for patients who are unfamiliar with features such as online appointment scheduling, or digital messaging. If vendors can ensure a positive first experience, it will also encourage patients to share their experiences with others, increasing patient portal awareness.
3. Almost half of patients report that their physician did not follow up with them after their visit. This is a significant opportunity for incentivizing patient portal usage.
47.9 percent of patients reported that their physician did not follow up with them after their last visit (outside of payment invoices). Of the physician’s that did follow up with patients, only 9.1 percent did so through a patient portal. Given the importance of patient engagement in modern healthcare, this represents an area for significant improvement.
Once physicians have shown patients how to use a portal, they still need to provide incentive for them to log on. Following up with each patient through these portals (which can automatically alert the patient by email that they have a new message to view) would help accomplish this goal. One of the ways for providers to meet the patient portals requirements for MU Stage 2 incentives is to have patients send them a secure message. The post-visit follow up provides a great opportunity for physicians to accomplish this goal.
To better encourage physician-patient communication (and therefore increased patient engagement), vendors can build in more robust automation capabilities, and place a larger emphasis on this during their training. Offering best-practice guides for engagement could also be a step in the right direction. If physician’s can easily automate the follow-up procedure based on information from the patient’s visit and the electronic health record, engagement rates (and portal usage) will likely increase.
Because it’s so easy to build software these days we’re seeing a proliferation of healthcare apps — what’s hard to figure out is whether we’re building the right software. Abder-Rahman Ali, currently pursuing his Medical Image Analysis Ph.D. in France, has graciously agreed to give us advice on how to write good software specifications for health and medical technology solutions. Some of you are probably rolling your eyes and thinking that software requirements specifications (SRS) are old and “tired” and we should be writing agile user stories instead. The reality is that in regulated and safety critical environments we still rely on SRS documents but the complex nature of systems of systems is making even those documents difficult to write. So, we’ll be talking requirements instead of user stories for now. The following is Abder-Rahman’s second installment, focused on vague and ambiguous requirements. He can be reached via e-mail or twitter.
Sometimes, when we pass by a software requirement that is not clear enough, we may say it is vague, and in another occasion, we may say it is ambiguous. Yes, we added such two features to that same requirement by that same person. But, did that person just use those two terms to refer to that that requirement was unclear? Does he just use those two terms interchangeably? Well, the bottom line, do vague and ambiguous mean the same at all? Or, they refer to two different things? This is what we will investigate in this article of our series.
Before beginning this topic, let us see how dictionaries define the terms vagueness and ambiguous.
Of the ways how Merrian-Webster dictionary defines the term vague is as follows:
Not clear in meaning: stated in a way that is general and not specific
Not thinking or expressing your thoughts clearly or precisely
Not completely formed or developed
Whereas, if we see how the same dictionary defines the term ambiguous, some of how it defines it is as follows:
Able to be understood in more than one way: having more than one possible meaning
I like how Diana Santos distinguishes between those two terms in her book, where she argues that vagueness is related to the language system, and thus, is considered systematic, and is a frequent property of the language. Whereas ambiguity is considered an unsystematic and accidental property.
Diana also stresses that for an expression to be vague between A and B, there should be a shared content. That is, having the same linguistic object doing double duty. In this case, we can consider the cases of “either A or not A” as an instances of vagueness. This Vagueness is automatically reflected in the speaker’s performance since it is an essential property of the linguistic system. But, if we go towards ambiguity, most ambiguities produced by speakers go unnoticed, since as we mentioned, it is considered unsystematic.
Put in another way, invitation to critical thinking takes us to the following; If we want to say that a term or expression is ambiguous, is to say that it has more than one conventional meaning. That is, it can be conventionally understood in more than one way.
Let us take an example. If you hear the word “bank”, what would it mean? Some of the meanings for “bank” are as follows:
The book also points out that for a term to be vague means that it is not entirely clear what it does and doesn’t apply to.
Cognitive Linguistics: Basic Readings, sums the above up, by mentioning that the difference between ambiguity and vagueness is a matter of whether two or more meanings associated with a given phonological form are distinct (ambiguous), or united as non-distinguished subcases of a single more general meaning (vague). An example for ambiguity as we mentioned is the term “bank”, where the meanings are quite separate. An example of vagueness is “aunt”, where it can mean “father’s sister” or “mother’s sister”, thus, the meanings are united into one meaning, that is, “parent’s sister”. So, the bottom line is that ambiguity corresponds to separation of different meanings, and vagueness corresponds to unity of different meanings.
So, I think we should now start admitting that there really exists a difference between those two terms, shouldn’t we?
Let us come back to software requirements. As Ben Rinzler mentions, the requirements statements must define a specific and testable outcome. That is, a way to measure that the requirement has been met has to be provided. But, such precision will diminish with vague and ambiguous requirements.
Rinzler continues; a vague requirement is that requirement that does not include enough information to establish exactly how to meet the requirement. On the other hand, an ambiguous requirement can have multiple meanings. Such requirement may sound precise, but defines the desired result in terms that can be interpreted in more than one way.
Let us take an example on both vague and ambiguous requirements to make this more clear.
An example of a vague requirement is the following:
The system shall be able to read updates from MedImg
The above requirement is considered vague since what will be “read” and what happens to the data was not specified.
This requirements can be improved as follows:
The system shall be able to import new tumor patient data supplied by MedImg to the radiology management system, for evaluating the tumor to be malignant or benign
An example of an ambiguous requirement is the following:
The system shall be able to provide historical reports
Here, we have to specify what we mean by “historical reports”. Thus, the requirement can be improved as follows:
The system shall be able to provide patient tumor data for the past five calendar years
We will end up the article here at the moment, so it doesn’t grow bigger for the reader. The main point to remember is that vagueness and ambiguous refer to two different terms, and they are factors that diminish the requirements’ precision and its ability to be measured and evaluated.
Stay tuned for the next articles, where we will dig more deep on how to deal and work with such uncertain requirements, and how can we present them in a precise manner.
“Large collections of electronic patient records have long provided abundant, but under-explored information on the real-world use of medicines. But when used properly these records can provide longitudinal observational data which is perfect for data mining,” Duan said. “Although such records are maintained for patient administration, they could provide a broad range of clinical information for data analysis. A growing interest has been drug safety.”
In this paper, the researchers proposed two novel algorithms—a likelihood ratio model and a Bayesian network model—for adverse drug effect discovery. Although the performance of these two algorithms is comparable to the state-of-the-art algorithm, Bayesian confidence propagation neural network, by combining three works, the researchers say one can get better, more diverse results.
I saw this a few weeks ago, and while I haven't had the time to delve deep into the details of this particular advance, it did at least give me more reason for hope with respect to the big picture of which it is a part.
It brought to mind the controversy over Vioxx starting a dozen or so years ago, documented in a 2004 article in the Cleveland Clinic Journal of Medicine. Vioxx, released in 1999, was a godsend to patients suffering from rheumatoid arthritic pain, but a longitudinal study published in 2000 unexpectedly showed a higher incidence of myocardial infarctions among Vioxx users compared with the former standard-of-care drug, naproxen. Merck, the patent holder, responded that the difference was due to a "protective effect" it attributed to naproxen rather than a causative adverse effect of Vioxx.
One of the sources of empirical evidence that eventually discredited Merck's defense of Vioxx's safety was a pioneering data mining epidemiological study conducted by Graham et al. using the live electronic medical records of 1.4 million Kaiser Permanente of California patients. Their findings were presented first in a poster in 2004 and then in the Lancet in 2005. Two or three other contemporaneous epidemiological studies of smaller non-overlapping populations showed similar results. A rigorous 18-month prospective study of the efficacy of Vioxx's generic form in relieving colon polyps showed an "unanticipated" significant increase in heart attacks among study participants.
Merck's withdrawal of Vioxx was an early victory for Big Data, though it did not win the battle alone. What the controversy did do was demonstrate the power of data mining in live electronic medical records. Graham and his colleagues were able to retrospectively construct what was effectively a clinical trial based on over 2 million patient-years of data. The fact that EMR records are not as rigorously accurate as clinical trial data capture was rendered moot by the huge volume of data analyzed.
Today, the value of Big Data in epidemiology is unquestioned, and the current focus is on developing better analytics and in parallel addressing concerns about patient privacy. The HITECH Act and Obamacare are increasing the rate of electronic biomedical data capture, and improving the utility of such data by requiring the adoption of standardized data structures and controlled vocabularies.
We are witnessing the dawning of an era, and hopefully the start of the transformation of our broken healthcare system into a learning organization.
I believe if we reduce the time between intention and action, it causes a major change in what you can do, period. When you actually get it down to two seconds, it’s a different way of thinking, and that’s powerful. And so I believe, and this is what a lot of people believe in academia right now, that these on-body devices are really the next revolution in computing.
I am convinced that wearable devices, in particular heads-up devices of which Google Glass is an example, will be playing a major role in medical practice in the not-too-distant future. The above quote from Thad Starner describes the leverage point such devices will exploit: the gap that now exists between deciding to make use of a device and being able to carry out the intended action.
Right now it takes me between 15 and 30 seconds to get my iPhone out and do something useful with it. Even in its current primitive form, Google Glass can do at least some of the most common tasks for which I get out my iPhone in under five seconds, such as taking a snapshot or doing a Web search.
Closing the gap between intention and action will open up potential computing modalities that do not currently exist, entirely novel use case scenarios that are difficult even to envision before a critical mass of early adopter experience is achieved.
The Technology Review interview from which I extracted the quote raises some of the potential issues wearable tech needs to address, but the value proposition driving adoption will soon be truly compelling.
I'm adding some drill-down links below.
Practices tended to use few formal mechanisms, such as formal care teams and designated care or case managers, but there was considerable evidence of use of informal team-based care and care coordination nonetheless. It appears that many of these practices achieved the spirit, if not the letter, of the law in terms of key dimensions of PCMH.
One bit of good news about the Patient Centered Medical Home (PCMH) model: here is a study showing that in spite of considerable challenges to PCMH implementation, the transformations it embodies can be and are being implemented even in small primary care practices serving disadvantaged populations.
We are delighted to introduce our new series of Health Insights. These free to attend events for healthcare professionals feature interactive round table activities, news on how the latest innovations support the health and care community, and best practice experiences from NHS Trust colleagues.
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readiness to hand
information storage and retrieval, access, efficiency, space, security, information sharing, patient safety, legibility
cost, savings, governance, reporting (locally, nationally, internationally), policy integration
Twitter, like the Internet in general, has become a vast source of and resource for health care information. As with other tools on the Internet it also has the potential for misinformation to be distributed. In some cases this is done by accident by those with the best intentions. In other cases it is done on purpose such as when companies promote their products or services while using false accounts they created.
In order to help determine the credibility of tweets containing health-related content I suggest the using the following checklist (adapted from Rains & Karmikel, 2009):
Ultimately it is up to the individual to determine how to use health information they find on Twitter or other Internet sources. For patients anecdotal or experiential information shared by others with the same illness may be considered very credible. Others conducting research may find this a less valuable information source. Conversely a researcher may only be looking for tweets that contain reference to peer-reviewed journal articles whereas patients and their caregivers may have little or no interest in this type of resource.
Rains, S. A., & Karmike, C. D. (2009). Health information-seeking and perceptions of website credibility: Examining Web-use orientation, message characteristics, and structural features of websites. Computers in Human Behavior, 25(2), 544-553.
The altmetric movement is intended to develop new measures of production and contribution in academia. The following article provides a primer for research scholars on what metrics they should consider collecting when participating in various forms of social media.
If you participate on Twitter you should be keeping track of the number of tweets you send, how many times your tweets are replied to, re-tweeted by other users and how many @mentions (tweets that include your Twitter handle) you obtain. ThinkUp is an open source application that allows you to track these metrics as well as other social media tools such as Facebook and Google +. Please read my extensive review about this tool. This service is free.
You should register with a domain shortening service such as bit.ly, which will provide you with an API key that you can enter into applications you use to share links. This will provide a means to keep track of your click-through statistics in one location. Bit.ly records how many times a link you created was clicked on, the referrer and location of the user. Consider registering your own domain name and using it to shorten your tweets as a means of branding. In addition, you can use your custom link on electronic copies of your CV or at your own web site. This will inform you when your links have been clicked on. You should also consider using bit.ly to create links used at your web site, providing you with feedback on which are used the most often. For example, all of the links in this article were created using my custom bit.ly domain. In addition, you can tweet a link to any research study you publish to publicize as well as keep track of how many clicks are obtained. Bit.ly is a free service.
Another tool to measure your tweets is TweetReach. This service allows you to track the reach of your tweets by Twitter handle or tweet. It provides output in formats that can be saved for use elsewhere (Excel, PDF or the option to print or save your output by link). To use these latter features you must sign up for an account but the service is free.
Buffer is a tool that allows you to schedule your tweets in advance. You can also connect Buffer to your bit.ly account so links used can be included in your overall analytics. Although Buffer provides its own measures on click-through counts this can contradict what appears in bit.ly. This service is free but also has paid upgrade options available that provide more detailed analytics.
Google Scholar Citation Profile
You can set up a profile with Google Scholar based on your publication record. The metrics provided by this service include a citation count, h-index and i10-index. When someone searches your name using Google Scholar your profile will appear at the top before any of the citations. This provides a quick way to separate your articles from someone else who has the same name as you.
Google Feedburner for RSS feeds
If you maintain your own web site and use RSS feeds to announce new postings you can also collect statistics on how many times your article is clicked on. Feedburner, recently acquired by Google provides one way to measure this. You enter your RSS feed ULR and a report is generate, which can be saved in CVS format.
Journal article download statistics
Many journals provide statistics on the number of downloads of articles. Keep track of those associated with your publication by visiting the site. For example, BioMed Central (BMC) maintains an access count of the last 30 days, one year and all time for each of your publications.
Other means of contributing to the knowledge base in your field include participating on web-based forums or web sites such as Quora. Quora provides threaded discussions on topics and allows participants to both generate and respond to the question. Other users vote on your responses and points are accrued. If you want another user to answer your question you must “spend” some of your points. Providing a link to your public profile on Quora on your CV will demonstrate another form of contribution to your field.
Paper.li is a free service that curates content and renders it in a web-based format. The focus of my Paper.li is the use of technology in Canadian Healthcare. I have also created a page that appears at my web site. Metrics on the number of times your paper has been shared via Facebook, Twitter, Google + and Linked are available. This service is free.
Twylah is similar to paper.li in that it takes content and displays it in a newspaper format except it uses your Twitter feed. There is an option to create a personalized page. I use tweets.lauraogrady.ca. I also have a Twylah widget at my web site that shows my trending tweets in a condensed magazine layout. It appears in the side bar. This free service does not yet provide metrics but can help increase your tweet reach. If you create a custom link for your Twylah page you can keep track of how many people visit it.
Analytics for your web site
Log file analysis
If you maintain your own web site you can use a variety of tools to capture and analyze its use. One of the most popular applications is Google Analytics. If you are using a content management system such as WordPress there are many plug-ins that will add the code to the pages at your site and produce reports. WordPress also provides a built-in analytic available through its dashboard.
If you have access to the raw log files you could use a shareware log file program or the open source tool Piwik. These tools will provide summaries about what pages of your site are visited most frequently, what countries the visitors come from, how long visitors remain at your site and what search terms are used to reach your site.
All of this information should be included in the annual report you prepare for your department and your tenure application. This will increase awareness of altmetrics and improve our ability to have these efforts “count” as contributions in your field.